wehub-resource-sync
60 lines
2.1 KiB
Markdown
60 lines
2.1 KiB
Markdown
Hierarchical Graph Representation Learning with Differentiable Pooling
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============
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Paper link: [https://arxiv.org/abs/1806.08804](https://arxiv.org/abs/1806.08804)
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Author's code repo: [https://github.com/RexYing/diffpool](https://github.com/RexYing/diffpool)
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This folder contains a DGL implementation of the DiffPool model. The first pooling layer is computed with DGL, and following pooling layers are computed with tensorized operation since the pooled graphs are dense.
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Dependencies
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------------
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* PyTorch 1.0+
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How to run
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----------
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```bash
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python train.py --dataset ENZYMES --pool_ratio 0.10 --num_pool 1 --epochs 1000
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python train.py --dataset DD --pool_ratio 0.15 --num_pool 1 --batch-size 10
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```
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Performance
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-----------
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ENZYMES 63.33% (with early stopping)
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DD 79.31% (with early stopping)
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## Update (2021-03-09)
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**Changes:**
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* Fix bug in Diffpool: the wrong `assign_dim` parameter
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* Improve efficiency of DiffPool, make the model independent of batch size. Remove redundant computation.
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**Efficiency:**
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On V100-SXM2 16GB
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| | Train time/epoch (original) (s) | Train time/epoch (improved) (s) |
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| ------------------ | ------------------------------: | ------------------------------: |
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| DD (batch_size=10) | 21.302 | **17.282** |
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| DD (batch_size=20) | OOM | **44.682** |
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| ENZYMES | 1.749 | **1.685** |
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| | Memory usage (original) (MB) | Memory usage (improved) (MB) |
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| ------------------ | ---------------------------: | ---------------------------: |
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| DD (batch_size=10) | 5274.620 | **2928.568** |
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| DD (batch_size=20) | OOM | **10088.889** |
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| ENZYMES | 25.685 | **21.909** |
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**Accuracy**
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Each experiment with improved model is only conducted once, thus the result may has noise.
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| | Original | Improved |
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| ------- | ---------: | ---------: |
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| DD | **79.31%** | 78.33% |
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| ENZYMES | 63.33% | **68.33%** |
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